NBP-based localization algorithm for wireless sensor networks in NLOS environments

To mitigate the impacts of non-line-of-sight（NLOS） errors on location accuracy, a non-parametric belief propagation（NBP）-based localization algorithm in the NLOS environment for wireless sensor networks is proposed.According to the amount of prior information known about the probabilities and distribution parameters of the NLOS error distribution, three different cases of the maximum a posterior（MAP） localization problems are introduced. The first case is the idealized case, i. e., the range measurements in the NLOS conditions and the corresponding distribution parameters of the NLOS errors are known. The probability of a communication of a pair of nodes in the NLOS conditions and the corresponding distribution parameters of the NLOS errors are known in the second case. The third case is the worst case, in which only knowledge about noise measurement power is obtained. The proposed algorithm is compared with the maximum likelihood-simulated annealing（ML-SA）-based localization algorithm. Simulation results demonstrate that the proposed algorithm provides good location accuracy and considerably outperforms the ML-SA-based localization algorithm for every case. The root mean square error（RMSE）of the location estimate of the NBP-based localization algorithm is reduced by about 1. 6 m in Case 1, 1. 8 m in Case 2 and 2. 3 m in Case 3 compared with the ML-SA-based localization algorithm. Therefore, in the NLOS environments,the localization algorithms can obtain the location estimates with high accuracy by using the NBP method.

Using self-location to calibrate the errors of observer positions for source localization

The uncertainty of observers＇ positions can lead to significantly degrading in source localization accuracy. This pa-per proposes a method of using self-location for calibrating the positions of observer stations in source localization to reduce the errors of the observer positions and improve the accuracy of the source localization. The relative distance measurements of the two coordinative observers are used for the linear minimum mean square error （LMMSE） estimator. The results of computer si-mulations prove the feasibility and effectiveness of the proposed method. With the general estimation errors of observers＇ positions, the MSE of the source localization with self-location calibration, which is significantly lower than that without self-location calibra-tion, is approximating to the Cramer-Rao lower bound （CRLB）.

An Improved Genetic Algorithm for UWB Localization

The UWB localization problem can be mapped as an optimization problem, which can be solved by genetic algorithm. In the localization process, the traditional fitness function does not include the ranging information between tags, resulting in insufficient ranging information and limited improvement of the localization accuracy. In view of this, an improved genetic localization algorithm is proposed. First, a new fitness function is constructed, which not only includes the ranging information between the tag and the base station, but also the ranging information between the tags to ensure that the ranging information is fully utilized in the localization process. Then, the search method based on Brownian motion is adopted to ensure that the improved algorithm can speed up the convergence speed of the localization result. The simulation results show that, compared with the traditional genetic localization algorithm, the improved genetic localization algorithm can reduce the influence of the ranging error on the localization error and improve the localization performance.

A digital authentication watermarking scheme for JPEG images with superior localization and security

The drawbacks of the current authentication watermarking schemes for JPEG images, which are inferior localization and the security flaws, are firstly analyzed in this paper. Then, two counterfeiting attacks are conducted on them. To overcome these drawbacks, a new digital authentication watermarking scheme for JPEG images with superior localization and security is proposed. Moreover, the probabilities of tamper detection and false detection are deduced under region tampering and collage attack separately. For each image block, the proposed scheme keeps four middle frequency points fixed to embed the watermark, and utilizes the rest of the DCT coefficients to generate 4 bits of watermark information. During the embedding process, each watermark bit is embedded in another image block that is selected by its corresponding secret key. Since four blocks are randomly selected for the watermark embedding of each block, the non-deterministic dependence among the image blocks is established so as to resist collage attack completely. At the receiver, according to judging of the extracted 4 bits of watermark information and the corresponding 9-neighbourhood system, the proposed scheme could discriminate whether the image block is tampered or not. Owing to the diminishing of false detection and the holding of tamper detection, we improve the accuracy of localization in the authentication process. Theoretic analysis and simulation results have proved that the proposed algorithm not only has superior localization, but also enhances the systematic security obviously.

Identifying and Quantifying Pixel-Level Uncertainty among Major Satellite Derived Global Land Cover Products

Accurate global land cover(GLC), as a key input for scientific communities, is important for a wide variety of applications. In order to understand the current suitability and limitation of GLC products, the discrepancy and pixellevel uncertainty in major GLC products in three epochs are assessed in this study by using an integrated uncertainty index(IUI) that combines the thematic uncertainty and local classification accuracy uncertainty. The results show that the overall spatial agreements(Ao values) between GLC products are lower than 58%, and the total areas of forests are very consistent in major GLC products, but significant differences are found in different forest classes.The misclassification among different forest classes and mosaic types can account for about 20% of the total disagreements. The mean IUI almost reaches 0.5, and high uncertainty mostly occurs in transition zones and heterogeneous areas across the world. Further efforts are needed to make in the land cover classifications in areas with high uncertainty. Designing a classification scheme for climate models, with explicit definitions of land cover classes in the threshold of common attributes, is urgently needed. Information of the pixel-level uncertainty in major GLC products not only give important implications for the specific application, but also provide a quite important basis for land cover fusion.